An Approach Of Features Extraction And Heatmaps Generation Based Upon Cnns And 3D Object Models

The rapid advancements in artificial intelligence have enabled recent progress of self-driving vehicles. However, the dependence on 3D object models and their annotations collected and owned by individual companies has become a major problem for the development of new algorithms. This thesis proposes an approach of directly using graphics models created from open-source datasets as the virtual representation of real-world objects. This approach uses Machine Learning techniques to extract 3D feature points and to create annotations from graphics models for the recognition of dynamic objects, such as cars, and for the verification of stationary and variable objects, such as buildings and trees. Moreover, it generates heat maps for the elimination of stationary/variable objects in real-time images before working on the recognition of dynamic objects. The proposed approach helps to bridge the gap between the virtual and physical worlds and to facilitate the development of new algorithms for self-driving vehicles

Advances in detecting object classes and their semantic parts

Object classes are central to computer vision and have been the focus of substantial research in the last fifteen years. This thesis addresses the tasks of localizing entire objects in images (object class detection) and localizing their semantic parts (part detection). We present four contributions, two for each task. The first two improve existing object class detection techniques by using context and calibration. The other two contributions explore semantic part detection in weakly-supervised settings. First, the thesis presents a technique for predicting properties of objects in an image based on its global appearance only. We demonstrate the method by predicting three properties: aspect of appearance, location in the image and class membership. Overall, the technique makes multi-component object detectors faster and improves their performance. The second contribution is a method for calibrating the popular Ensemble of Exemplar- SVM object detector. Unlike the standard approach, which calibrates each Exemplar- SVM independently, our technique optimizes their joint performance as an ensemble. We devise an efficient optimization algorithm to find the global optimal solution of the calibration problem. This leads to better object detection performance compared to using independent calibration. The third innovation is a technique to train part-based model of object classes using data sourced from the web. We learn rich models incrementally. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. Importantly, it does not require any part location annotation, which is one of the main limits to training many part detectors. Finally, the last contribution is a study on whether semantic object parts emerge in Convolutional Neural Networks trained for higher-level tasks, such as image classification. While previous efforts studied this matter by visual inspection only, we perform an extensive quantitative analysis based on ground-truth part location annotations. This provides a more conclusive answer to the question

Richer object representations for object class detection in challenging real world images

Object class detection in real world images has been a synonym for object localization for the longest time. State-of-the-art detection methods, inspired by renowned detection benchmarks, typically target 2D bounding box localization of objects. At the same time, due to the rapid technological and scientific advances, high-level vision applications, aiming at understanding the visual world as a whole, are coming into the focus. The diversity of the visual world challenges these applications in terms of representational complexity, robust inference and training data. As objects play a central role in any vision system, it has been argued that richer object representations, providing higher level of detail than modern detection methods, are a promising direction towards understanding visual scenes. Besides bridging the gap between object class detection and high-level tasks, richer object representations also lead to more natural object descriptions, bringing computer vision closer to human perception. Inspired by these prospects, this thesis explores four different directions towards richer object representations, namely, 3D object representations, fine-grained representations, occlusion representations, as well as understanding convnet representations. Moreover, this thesis illustrates that richer object representations can facilitate high-level applications, providing detailed and natural object descriptions. In addition, the presented representations attain high performance rates, at least on par or often superior to state-of-the-art methods.Detektion von Objektklassen in natürlichen Bildern war lange Zeit gleichbedeutend mit Lokalisierung von Objekten. Von anerkannten Detektions-Benchmarks inspirierte Detektionsmethoden, die auf dem neuesten Stand der Forschung sind, zielen üblicherweise auf die Lokalisierung von Objekten im Bild. Gleichzeitig werden durch den schnellen technologischen und wissenschaftlichen Fortschritt abstraktere Bildverarbeitungsanwendungen, die ein Verständnis der visuellen Welt als Ganzes anstreben, immer interessanter. Die Diversität der visuellen Welt ist eine Herausforderung für diese Anwendungen hinsichtlich der Komplexität der Darstellung, robuster Inferenz und Trainingsdaten. Da Objekte eine zentrale Rolle in jedem Visionssystem spielen, wurde argumentiert, dass reichhaltige Objektrepräsentationen, die höhere Detailgenauigkeit als gegenwärtige Detektionsmethoden bieten, ein vielversprechender Schritt zum Verständnis visueller Szenen sind. Reichhaltige Objektrepräsentationen schlagen eine Brücke zwischen der Detektion von Objektklassen und abstrakteren Aufgabenstellungen, und sie führen auch zu natürlicheren Objektbeschreibungen, wodurch sie die Bildverarbeitung der menschlichen Wahrnehmung weiter annähern. Aufgrund dieser Perspektiven erforscht die vorliegende Arbeit vier verschiedene Herangehensweisen zu reichhaltigeren Objektrepräsentationen