Doctor of Philosophy

dissertation3D reconstruction from image pairs relies on finding corresponding points between images and using the corresponding points to estimate a dense disparity map. Today's correspondence-finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3D computer vision applications, however, don't produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. Additionally, traditional stereo correspondence-finding techniques which use image features or pixel intensities sometimes produce inaccurate results. This thesis presents a novel image correspondence-finding technique that aligns pairs of image sequences using the optical flow fields. The optical flow fields provide information about the structure and motion of the scene which is not available in still images, but which can be used to align images taken from different camera positions. The method applies to applications where there is inherent motion between the camera rig and the scene and where the scene has enough visual texture to produce optical flow. We apply the technique to a traditional binocular stereo rig consisting of an RGB/IR camera pair and to a coaxial camera rig. We present results for synthetic flow fields and for real images sequences with accuracy metrics and reconstructed depth maps

Detecting semantic concepts in digital photographs: low-level features vs. non-homogeneous data fusion

Semantic concepts, such as faces, buildings, and other real world objects, are the most preferred instrument that humans use to navigate through and retrieve visual content from large multimedia databases. Semantic annotation of visual content in large collections is therefore essential if ease of access and use is to be ensured. Classification of images into broad categories such as indoor/outdoor, building/non-building, urban/landscape, people/no-people, etc., allows us to obtain the semantic labels without the full knowledge of all objects in the scene. Inferring the presence of high-level semantic concepts from low-level visual features is a research topic that has been attracting a significant amount of interest lately. However, the power of lowlevel visual features alone has been shown to be limited when faced with the task of semantic scene classification in heterogeneous, unconstrained, broad-topic image collections. Multi-modal fusion or combination of information from different modalities has been identified as one possible way of overcoming the limitations of single-mode approaches. In the field of digital photography, the incorporation of readily available camera metadata, i.e. information about the image capture conditions stored in the EXIF header of each image, along with the GPS information, offers a way to move towards a better understanding of the imaged scene. In this thesis we focus on detection of semantic concepts such as artificial text in video and large buildings in digital photographs, and examine how fusion of low-level visual features with selected camera metadata, using a Support Vector Machine as an integration device, affects the performance of the building detector in a genuine personal photo collection. We implemented two approaches to detection of buildings that combine content-based and the context-based information, and an approach to indoor/outdoor classification based exclusively on camera metadata. An outdoor detection rate of 85.6% was obtained using camera metadata only. The first approach to building detection, based on simple edge orientation-based features extracted at three different scales, has been tested on a dataset of 1720 outdoor images, with a classification accuracy of 88.22%. The second approach integrates the edge orientation-based features with the camera metadata-based features, both at the feature and at the decision level. The fusion approaches have been evaluated using an unconstrained dataset of 8000 genuine consumer photographs. The experiments demonstrate that the fusion approaches outperform the visual features-only approach by of 2-3% on average regardless of the operating point chosen, while all the performance measures are approximately 4% below the upper limit of performance. The early fusion approach consistently improves all performance measures

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model

Assigning Main Orientation to an EOH Descriptor on Multispectral Images

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment

30 años (1977-2007): Centro de Investigaciones Ópticas (CIOp)

La edición de este libro fue financiada en parte por la Comisión de Investigaciones Científicas de la Provincia de Buenos Aires

Addendum: Li, Y.; Shi, X.; Wei, L.; Zou, J.; Chen, F. Assigning Main Orientation to an EOH Descriptor on Multispectral Images. Sensors 2015, 15, 15595–15610.

The authors wish to update the Acknowledgments section in their paper published in Sensors [1], doi:10.3390/s150715595, http://www.mdpi.com/1424-8220/15/7/15595. [...