Object recognition in infrared imagery using appearance-based methods

Abstract unavailable please refer to PD

Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Includes bibliographical references (pages 29-30).Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard

Recognising faces in unseen modes: a tensor based approach

This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost

Compressing the illumination-adjustable images with principal component analysis.

Pun-Mo Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 90-95).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Existing Approaches --- p.2Chapter 1.3 --- Our Approach --- p.3Chapter 1.4 --- Structure of the Thesis --- p.4Chapter 2 --- Related Work --- p.5Chapter 2.1 --- Compression for Navigation --- p.5Chapter 2.1.1 --- Light Field/Lumigraph --- p.5Chapter 2.1.2 --- Surface Light Field --- p.6Chapter 2.1.3 --- Concentric Mosaics --- p.6Chapter 2.1.4 --- On the Compression --- p.7Chapter 2.2 --- Compression for Relighting --- p.7Chapter 2.2.1 --- Previous Approaches --- p.7Chapter 2.2.2 --- Our Approach --- p.8Chapter 3 --- Image-Based Relighting --- p.9Chapter 3.1 --- Plenoptic Illumination Function --- p.9Chapter 3.2 --- Sampling and Relighting --- p.11Chapter 3.3 --- Overview --- p.13Chapter 3.3.1 --- Codec Overview --- p.13Chapter 3.3.2 --- Image Acquisition --- p.15Chapter 3.3.3 --- Experiment Data Sets --- p.16Chapter 4 --- Data Preparation --- p.18Chapter 4.1 --- Block Division --- p.18Chapter 4.2 --- Color Model --- p.23Chapter 4.3 --- Mean Extraction --- p.24Chapter 5 --- Principal Component Analysis --- p.29Chapter 5.1 --- Overview --- p.29Chapter 5.2 --- Singular Value Decomposition --- p.30Chapter 5.3 --- Dimensionality Reduction --- p.34Chapter 5.4 --- Evaluation --- p.37Chapter 6 --- Eigenimage Coding --- p.39Chapter 6.1 --- Transform Coding --- p.39Chapter 6.1.1 --- Discrete Cosine Transform --- p.40Chapter 6.1.2 --- Discrete Wavelet Transform --- p.47Chapter 6.2 --- Evaluation --- p.49Chapter 6.2.1 --- Statistical Evaluation --- p.49Chapter 6.2.2 --- Visual Evaluation --- p.52Chapter 7 --- Relighting Coefficient Coding --- p.57Chapter 7.1 --- Quantization and Bit Allocation --- p.57Chapter 7.2 --- Evaluation --- p.62Chapter 7.2.1 --- Statistical Evaluation --- p.62Chapter 7.2.2 --- Visual Evaluation --- p.62Chapter 8 --- Relighting --- p.65Chapter 8.1 --- Overview --- p.66Chapter 8.2 --- First-Phase Decoding --- p.66Chapter 8.3 --- Second-Phase Decoding --- p.68Chapter 8.3.1 --- Software Relighting --- p.68Chapter 8.3.2 --- Hardware-Assisted Relighting --- p.71Chapter 9 --- Overall Evaluation --- p.81Chapter 9.1 --- Compression of IAIs --- p.81Chapter 9.1.1 --- Statistical Evaluation --- p.81Chapter 9.1.2 --- Visual Evaluation --- p.86Chapter 9.2 --- Hardware-Assisted Relighting --- p.86Chapter 10 --- Conclusion --- p.89Bibliography --- p.9

A Review on Facial Expression Recognition Techniques

Facial expression is in the topic of active research over the past few decades. Recognition and extracting various emotions and validating those emotions from the facial expression become very important in human computer interaction. Interpreting such human expression remains and much of the research is required about the way they relate to human affect. Apart from H-I interfaces other applications include awareness system, medical diagnosis, surveillance, law enforcement, automated tutoring system and many more. In the recent year different technique have been put forward for developing automated facial expression recognition system. This paper present quick survey on some of the facial expression recognition techniques. A comparative study is carried out using various feature extraction techniques. We define taxonomy of the field and cover all the steps from face detection to facial expression classification