Monocular SLAM Supported Object Recognition

In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.Comment: Accepted to appear at Robotics: Science and Systems 2015, Rome, Ital

Object Detection using Dimensionality Reduction on Image Descriptors

The aim of object detection is to recognize objects in a visual scene. Performing reliable object detection is becoming increasingly important in the fields of computer vision and robotics. Various applications of object detection include video surveillance, traffic monitoring, digital libraries, navigation, human computer interaction, etc. The challenges involved with detecting real world objects include the multitude of colors, textures, sizes, and cluttered or complex backgrounds making objects difficult to detect. This thesis contributes to the exploration of various dimensionality reduction techniques on descriptors for establishing an object detection system that achieves the best trade-offs between performance and speed. Histogram of Oriented Gradients (HOG) and other histogram-based descriptors were used as an input to a Support Vector Machine (SVM) classifier to achieve good classification performance. Binary descriptors were considered as a computationally efficient alternative to HOG. It was determined that single local binary descriptors in combination with Support Vector Machine (SVM) classifier don\u27t work as well as histograms of features for object detection. Thus, histogram of binary descriptors features were explored as a viable alternative and the results were found to be comparable to those of the popular Histogram of Oriented Gradients descriptor. Histogram-based descriptors can be high dimensional and working with large amounts of data can be computationally expensive and slow. Thus, various dimensionality reduction techniques were considered, such as principal component analysis (PCA), which is the most widely used technique, random projections, which is data independent and fast to compute, unsupervised locality preserving projections (LPP), and supervised locality preserving projections (SLPP), which incorporate non-linear reduction techniques. The classification system was tested on eye detection as well as different object classes. The eye database was created using BioID and FERET databases. Additionally, the CalTech-101 data set, which has 101 object categories, was used to evaluate the system. The results showed that the reduced-dimensionality descriptors based on SLPP gave improved classification performance with fewer computations

Indexing Iris Database Using Multi-Dimensional R-Trees

Iris is one of the most widely used biometric modality for recognition due to its reliability, non-invasive characteristic, speed and performance. The patterns remain stable throughout the lifetime of an individual. Attributable to these advantages, the application of iris biometric is increasingly encouraged by various commercial as well as government agencies. Indexing is done to identify and retrieve a small subset of candidate data from the database of iris data of individuals in order to determine a possible match. Since the database is extremely large, it is necessary to find fast and efficient indexing methods. In this thesis, an efficient local feature based indexing approach is proposed using clustered scale invariant feature transform (SIFT) keypoints, that achieves invariance to similarity transformations, illumination and occlusion. These cluster centers are used to construct R-trees for indexing. This thesis proposes an application of R-trees for iris database indexing. The system is tested using publicly available BATH and CASIA-IrisV4 databases

Efficient Retrieval and Categorization for 3D Models based on Bag-of-Words Approach

Ph.DDOCTOR OF PHILOSOPH

Algorithms for people re-identification from RGB-D videos exploiting skeletal information

In this thesis a novel methodology to face people re-identification problem is proposed. Re-identification is a complex research topic representing a fundamental issue especially for intelligent video surveillance applications. Its goal is to determine the occurrences of the same person in different video sequences or images, usually by choosing from a high number of candidates within a datasetope

SketchSeeker : Finding Similar Sketches

Searching is an important tool for managing and navigating the massive amounts of data available in today’s information age. While new searching methods have be-come increasingly popular and reliable in recent years, such as image-based searching, these methods are more limited than text-based means in that they don’t allow generic user input. Sketch-based searching is a method that allows users to draw generic search queries and return similar drawn images, giving more user control over their search content. In this thesis, we present Sketchseeker, a system for indexing and searching across a large number of sketches quickly based on their similarity. The system includes several stages. First, sketches are indexed according to efficient and compact sketch descriptors. Second, the query retrieval subsystem considers sketches based on shape and structure similarity. Finally, a trained support vector machine classifier provides semantic filtering, which is then combined with median filtering to return the ranked results. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows significant improvements in both speed and accuracy when compared to existing known techniques. The focus of this thesis is to outline the general components of a sketch retrieval system to find near similar sketches in real time